Abstract
This paper proposes a computationally inexpensive algorithm that uses player data to optimize NPC pathfinding in a competitive, multiplayer environment. Statistics gathered during matches are subjected to pattern analysis and used to modify edge values of the map graph. Utilizing data describing player habits enhances the AI’s odds against the player by letting it better adapt to the situation. Combining the data concerning individual players results in the possibility to react to multiple human opponents at once, maintaining the ability to adapt even after a large number of matches. In order to further improve control over the agent, two novel variables are introduced, increasing the ability to adapt to behavior that is unique in a particular match and giving more control over the risk the agent is willing to take. The findings of the hereby paper can be applied to any pathfinding algorithm that works with directed graphs and used by robots and real life agents.
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Stawarz, P., Świder, Z. (2017). Data-Driven Video Game Agent Pathfinding. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2017. ICA 2017. Advances in Intelligent Systems and Computing, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-319-54042-9_28
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DOI: https://doi.org/10.1007/978-3-319-54042-9_28
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